Building Public Interest AI Catalytic Funding for Equitable Compute Access
20 Feb 2026 11:00h - 12:00h
Building Public Interest AI Catalytic Funding for Equitable Compute Access
Summary
The session opened by Deepali Khanna framed the emerging “compute divide” as the new digital divide that will shape who can lead AI development, noting that access to GPUs and cloud capacity is the main constraint on AI progress [4-7]. She highlighted India’s AI mission, which is deploying more than 38,000 public-sector GPUs to create a large-scale, sovereign yet open compute ecosystem for the Global South [14-18]. Khanna positioned philanthropy as a catalyst that can reduce risk, unlock capital and forge partnerships to accelerate democratization, outlining three discussion acts on what to democratize, how South-South partnerships and financing help, and concrete commitments [22-27][30-33].
Sushant Kumar announced the release of a report produced by the Democratizing AI Resources Working Group led by Dr Saurabh Garg, inviting feedback over the coming months [44-48][50-51]. Garg described the AI Summit’s guiding “sutras” of people, planet and progress and identified six foundational pillars-compute, capability, collaboration, connectivity, compliance and context-to guide a roadmap for equitable AI [56-68]. He emphasized that compute is today’s defining barrier and argued for shared, affordable infrastructure, capability diffusion through joint research, and robust yet flexible governance, proposing the open-source “Maitri” platform as a digital public good that countries can adapt [69-75][76-79]. Garg also warned that future model designs could reduce the current heavy compute demand, suggesting that smaller, domain-specific models might alleviate the energy-intensive barrier [84-89].
In response to a question about India’s governance model for public-interest compute, Garg said the focus should be on intelligent prioritization rather than rationing, and that philanthropic actors can help ensure affordable access [109-112]. Martin Tisné cautioned that building compute capacity alone can create “white-elephant” data centres if not paired with contextual data and open-source software, noting that most open-source funding comes from large corporations and that the ecosystem’s critical dependencies are under-resourced [125-138][139-140]. Vilas Dhar argued that sovereignty should be reframed from a territorial notion to an active, participatory model of AI diffusion, calling for new institutional intermediaries-such as those exemplified by Kalpa Impact-to connect talent, policy and capital for public-interest AI [163-186][187-191]. Dr Shikha Gitao presented an Africa compute-demand index, estimating a need for 2.5 million GPU-hours annually and highlighting the gap between demand and current supply, while stressing that investment readiness-including power, talent and use-cases-is essential for effective South-South collaboration [223-250][260-284]. Shaun Seow suggested that compute may be overrated compared with energy and application layers, pointed out latency and data-sovereignty limits to sharing compute across regions, and proposed aggregating demand to negotiate better cloud pricing and using philanthropy to subsidize costs and close skills gaps [310-333]. The panel concluded that future efforts must move beyond hardware to comprehensive public-interest frameworks covering models, data, talent and governance, as emphasized by Garg’s final call for broader systemic work [361-362].
Keypoints
Major discussion points
– The emerging “compute divide” and the need to democratize AI infrastructure.
Deepali frames the problem as a shift from a digital to a compute divide, stressing that access to GPUs and cloud capacity will decide who shapes AI’s future [4-7][14-18]. Dr. Garg’s working-group identifies compute as the “defining barrier” and outlines six pillars (compute, capability, collaboration, connectivity, compliance, context) to guide a roadmap [68-70].
– A shared, multi-stakeholder platform - Maitri – as a digital public good.
The group proposes a voluntary, modular platform (M-A-I-T-R-I) that countries can adopt and customize to expand shared access to compute, data, and governance [76-79].
– Beyond hardware: data access, open-source ecosystems, talent and governance are equally critical.
Panelists warn that simply building data centres can create “white-elephant” resources if local data, open-source tooling, and skilled people are missing [128-133][148-152][274-283]. They call for robust, flexible governance and new funding models for open-source dependencies [132-138].
– South-South partnerships and catalytic philanthropy as levers for equitable AI diffusion.
The Rockefeller Foundation positions philanthropy as a risk-reducer and capital-unlocker [23]; Dr. Garg stresses “intelligent prioritization” of compute for public-interest work, with philanthropy playing a key role [109-112]. Vilas and Martin discuss the need for new institutional intermediaries that can translate compute capacity into concrete outcomes for developing economies [155-166][184-191].
– Practical metrics and institutional readiness: demand indexes, investment readiness, latency, and energy constraints.
Dr. Shikha introduces a “Compute Demand Index” and an “AI Investment Readiness Index” to quantify GPU-hour needs and the capacity of countries to use them [223-236][241-246]. Shaun highlights physical limits such as latency and energy that affect cross-border compute sharing [322-328], while later remarks stress the importance of aligning compute with local use-cases and building resilient, relational notions of sovereignty [340-354].
Overall purpose / goal of the discussion
The session moves the conversation from diagnosing the global compute gap to outlining actionable pathways: defining what AI resources must be democratized, exploring South-South collaborations and catalytic financing, and committing to concrete institutional mechanisms (e.g., the Maitri platform, demand/readiness indexes) that can be launched within the year [24-33][27-30][31-33].
Tone of the discussion
– Opening: Optimistic and urgent, emphasizing AI’s transformative promise and India’s pioneering public-interest compute rollout.
– Middle: Analytical and cautionary, with panelists highlighting gaps in data, open-source funding, talent, and the risk of “white-elephant” infrastructure.
– Later: Collaborative and solution-focused, proposing concrete tools (Maitri, indexes) and calling for new institutional intermediaries and philanthropic catalysts.
– Closing: Hopeful yet realistic, acknowledging the complexity of scaling compute while reaffirming commitment to equitable, public-interest AI outcomes.
Speakers
– Deepali Khanna – Senior leader at the Rockefeller Foundation, focusing on AI democratization and public interest AI infrastructure. [S1]
– Dr. Saurabh Garg – Secretary, Ministry of Statistics and Programme Implementation, Government of India; Chair of the Democratizing AI Resources Working Group. [S4]
– Andrew Sweet – Vice President, Rockefeller Foundation; moderator for the panel discussion. [S7]
– Shaun Seow – CEO, Philanthropy Asia Alliance; former senior roles at Temasek and CEO of Mediacorp. [S8]
– Dr. Shikha Gitao – Founder and CEO, Kala AI (Kenya); leader in AI access and compute initiatives for Africa. [S9]
– Sushant Kumar – Partner at Kalpa Impact, collaborator on the AI democratization report.
– Vilas Dhar – President, Patrick J. McGovern Foundation; member of the UN Secretary-General’s High-Level Advisory Board on AI. [S13][S15]
– Martin Tisné – Founder, Current AI; Public-Interest Envoy for France’s AI Action Summit. [S16]
Additional speakers:
– Shri Abhishek Singh – Mentioned for leadership and partnership support (role not specified).
– Charu – Acknowledged for extensive work in organizing the session (role not specified).
– Dr. Sarabgarg – Referred to in the opening remarks; likely the same individual as Dr. Saurabh Garg (role not specified).
– Velas – Participant referenced during the discussion on data stewardship (role not specified).
– Anish – Member of the Kalpa Impact team (role not specified).
– Jennifer – Member of the Kalpa Impact team (role not specified).
Opening Remarks – Deepali Khanna
Deepali Khanna opened the session by noting that the promise of artificial intelligence is now limited not by imagination but by a “compute divide” – unequal access to GPUs, cloud capacity and scalable infrastructure that will decide who gets to shape AI’s future [4-7]. She highlighted India’s AI mission, which is mobilising more than 38 000 public-sector GPUs to create one of the world’s most ambitious sovereign-yet-open compute ecosystems for the Global South [14-18]. Khanna framed the discussion in a three-act structure – defining what to democratize, exploring South-South partnerships and financing, and securing concrete commitments [22-27]. She thanked the leaders supporting the effort – Shri Abhishek Singh, Dr Saurabh Garg, Charu, Martin Tisné, Vilas Dhar, Sean, Dr Shikha Gitao, and the Kalpa Impact team [30-33].
Report Launch – Sushant Kumar
Sushant Kumar announced the release of the report “Opening up Computational Resources for New AI Futures”, produced by the Democratising AI Resources Working Group under Dr Saurabh Garg’s leadership [44-48][50-51]. He invited participants to provide feedback over the coming months [44-48].
Keynote – Dr Saurabh Garg
Dr Garg, chair of the working group, reminded the audience of the AI Summit’s three guiding “sutras” – people, planet and progress – and quoted the summit’s mandate: “AI must serve human welfare, advance sustainable development and enable shared prosperity” [60-62]. He noted that the summit convened seven working groups [60-62]. Garg outlined six foundational pillars for a collective roadmap: compute, capability, collaboration, connectivity, compliance and context [68-70]. He described compute as today’s defining barrier, with GPUs and high-performance clusters concentrated in a few regions, and argued that affordable, shared infrastructure is essential [69-71]. To address this, the group is prototyping Maitri – a non-binding, voluntary, modular digital public good that countries can adopt, customise and build upon, facilitating shared access to compute, data and governance [76-79]. Vishal Sikka warned that future model designs might shift from today’s energy-intensive large-scale architectures toward smaller, domain-specific models, likening the trade-off to “calorie vs. gigawatt” considerations [84-89]. When asked how India’s compute programme would be governed if treated as a public utility, Garg replied that the focus should be on “intelligent prioritisation” rather than strict rationing, positioning the platform as an enabling public-good that philanthropy can help fund to ensure affordable access for public-interest projects [109-112].
Panel Discussion
Moderator – Andrew Sweet
– Martin Tisné (on moving from AI consumer to co-creator) warned that simply building compute capacity risks creating “white-elephant” data centres that sit idle without contextual data, language resources and open-source tooling [125-128]. He stressed that data innovation, especially privacy-preserving sharing mechanisms, lags far behind compute advances, and that most open-source funding comes from large corporations, leaving critical low-tier dependencies under-resourced [132-138]. Tisné concluded that effective AI diffusion requires coordinated attention to compute, data and open-source ecosystems [139-140].
– Vilas Dhar reframed sovereignty, arguing that it should shift from a Westphalian, territorial model to an active, participatory approach that builds institutions capable of translating compute into locally relevant outcomes [163-166][170-176]. He likened the needed institutional framework to the Indian Premier League’s model of world-class, inclusive organisations, and called for new intermediaries-such as Culpa Impact-that can connect talent, policy and capital to public-interest AI [185-191][310-313]. Dhar warned against a “trickle-down” view of AI diffusion, advocating instead for interdependent, mutually beneficial partnerships that move beyond competition [350-354].
– Dr Shikha Gitao presented a concrete “Compute Demand Index” for Africa, estimating a need for 2.5 million GPU-hours annually (rising to 7.5 million over three years) [223-226][243-246] and highlighting that the continent currently possesses only about 5 % of this capacity [250-254]. She introduced an “AI Investment Readiness Index” to assess whether countries have the power, talent, data and use-cases required to make compute effective [231-236]. Gitao argued that without clear use-cases-e.g., health, education or agriculture-donated GPUs remain idle, and she called for South-South collaborations where India could allocate specific GPU-hour blocks to African nations based on demand [292-298][300-304].
– Shaun Seow, CEO of Philanthropy Asia Alliance, offered a contrasting perspective, suggesting that compute is “overrated” compared with energy and application layers [310-314]. He highlighted physical constraints such as latency of 50-100 ms over 10 000 km [322-328] and data-residency regulations that make direct cross-border compute sharing between, for example, India and Indonesia impractical. Seow proposed aggregating demand across countries to negotiate better cloud pricing [331-334] and using philanthropy to subsidise compute for startups and impact organisations, while also noting the urgent need to close the skills gap in Asia [335-339].
– In a later turn, Martin Tisné reflected on sovereignty, distinguishing traditional territorial control from “relational” sovereignty exemplified by indigenous data-ownership concepts, and advocated for a global, collaborative stack that balances control with agency [336-339].
– Vilas Dhar responded by emphasizing the need for participatory institutions that foster interdependence rather than competition, and he called for concrete institutional building blocks within the next twelve months to link compute provision with talent development, data stewardship and policy [340-354].
Closing Remarks – Dr Saurabh Garg & Andrew Sweet
Dr Garg reiterated that democratization must extend beyond hardware to include models, data, talent and interoperable governance frameworks, urging the community to develop public-interest standards that address the full AI stack [361-362]. Andrew Sweet thanked the Indian government, Kalpa Impact and all panelists, announced that the report would be publicly available for comment until 31 March, and invited participants to continue the conversation beyond the summit [363-365].
Consensus & Divergences
Across the session, participants agreed that the compute divide is a critical barrier, that philanthropy can act as a catalyst, and that robust, flexible governance is essential for moving nations from AI consumers to co-creators. Disagreements emerged around the primary bottleneck-whether compute, data or broader investment readiness should be prioritised-and over the feasibility of cross-border compute sharing, with Shaun Seow highlighting technical and regulatory limits while Dr Gitao advocated for South-South GPU-hour allocations. The dialogue also revealed divergent views on sovereignty: Martin Tisné promoted relational, indigenous-data models, whereas Vilas Dhar called for new participatory institutions that transcend territorial notions. These nuanced debates underscore the need for hybrid approaches that combine shared infrastructure (e.g., Maitri), measurable demand and readiness metrics, targeted philanthropic financing, and innovative institutional intermediaries to achieve equitable, public-interest AI outcomes.
to be with us, so thank you. We are here because we believe in AI’s transformative potential, and I’m certain you’ve heard a great deal about it over the past few days. Today, this session is about something deeper. The digital divide is rapidly becoming a compute divide. AI today is not constrained by imagination. It is constrained by infrastructure, by who has access to GPUs, to cloud capacity, to scalable compute. And that divide will determine who shapes the future of AI. Democratization in this context is not about catching up. It is about expanding who gets to lead. It is about ensuring that the next generation of AI breakthroughs are not concentrated in a handful of geographies, but are shaped by diverse talent, languages, and lived realities across the world.
And here, India is not waiting for permission. India is not waiting for permission. India is showing that it can be done differently. Through the India AI mission and through the compute capacity plan, mobilizing more than 38 ,000 GPUs as public infrastructure, India is building one of the most ambitious public interest compute ecosystems anywhere in the world. This is not incremental reform. This is infrastructure at scale. This is sovereign capability combined with openness. India is demonstrating that public interest AI infrastructure can be built in the Global South by the Global South and for the Global South. And this leadership matters because equitable access to compute is not just about hardware. It sits alongside access to data, open source models, talent, and institutional capacity.
India is proving that you can design AI ecosystems that are both globally competitive and globally competitive. And locally grounded. At the Rockefeller Foundation, we believe this moment requires moving from diagnosis to action. Philanthropy’s role is to be catalytic, to reduce risk, unlock capital, and convene unlikely partnerships that accelerate progress. Over the next hour, our discussion will unfold in three acts. First, what exactly are we democratizing? That’s an important question. Second, how do South -South partnerships and catalytic financing accelerate progress? And third, what concrete commitments can we land this year? If India’s example shows us anything, it is this. Democratization is not theoretical. It is operational. It is scalable. And it is already underway. The question now is how we accelerate it together.
Before we begin, let me take a moment to acknowledge a few leaders in the room. Shri Abhishek Singh who unfortunately has been pulled into another meeting but his leadership has been amazing his steadfast partnership and support has been something that I am extremely grateful for his vision of guiding this important work with clarity has been just spectacular Dr. Sarabgarg we are honored by your presence you have been in sessions since this morning and thank you for your leadership it’s truly a privilege to have you with us today my colleague Andrew Sweet who has joined us from across the world one of the sharpest lines of the Rockefeller Foundation and truly a force for good thank you for being with us today and supporting this conversation and of course I want to also thank Charu who has been working endlessly and very hard to kind of get us to this place thank you Charu for your leadership Martin, Vilas, Sean and Dr.
Shikoh thank you for lending yourself your voice and expertise to today’s discussion Your perspectives will help ground this dialogue in both ambition and action, and I know all of you are action -oriented folks, so we’re going to have something really cool come out from here. And last but certainly not least, our partners at Kalpa Impact, Sushant, Anish, Jennifer, thank you for being extraordinary collaborators and for helping shape today’s session. It is now my pleasure to hand it over to Dr. Gorf. Please, over to you, sir, or maybe I’ll hand it over. Okay.
Thank you. Thank you, Deepali. When I mentioned the report, I fumbled the name, so I’ll go again. Opening up computational resources for new AI futures, new AI world is possible. And this is something that the team has worked really hard over the last few months. And today is an opportunity when we release a working version of that report and invite inputs, feedback, comments, and suggestions, which we will work through over the next few months. This research helped us think through and work with the Democratizing AI Resources Working Group under the leadership of Dr. Saurabh Garg. And he’s here. So it’s a pleasure and a privilege for us to invite him. And the other panelists to release this report.
Thank you. Thank you. Thank you. Thank you. opening up computational resources or in fact all resources that are necessary for development of AI in public interest and for real world impact. I could think of no better person than Dr. Saurav Garg under whose leadership I think we have come a long way in not just the intellectual thinking but as he will tell you in terms of operationalizing how we can bring this to life for billions in the global south and also the other countries in the world. Dr. Saurabh Garg, please for your keynote.
Thank you and colleagues panelists great to be here and great to see a large kind of attendance that we have seen over the past few days in the AI summit and And there were seven working groups set up under the AI Summit umbrella. And one of them was on democratizing AI resources. I had the privilege to chair that group along with Kenya and Egypt. So I’ll obviously talk a bit on that. But before that, just to say that I think all of us are of the opinion that AI will definitely transform the world. I think the question is whether that transformation would be equitable, would be inclusive and aligned with public interest. And I think that’s really the issue which concerns a lot of people.
The AI Summit itself was built around three guiding sources. Sutras, the people, planet and progress. And therefore, the concept being that AI ultimately must serve human welfare. advance sustainable development and enable shared prosperity. I think these would be key background in the way these sutras were developed. And obviously, democratizing many of these resources would be key to that. During our working group discussions, we had the opportunity to talk to a large number of countries, people from academia, civil society, and other international organizations. And I think one consistent message was that most countries are not really seeking only access to AI, but also seeking agency in AI. And I think that’s key. And how the AI systems need to reflect each country’s own development priorities, languages, and social contexts.
From these discussions, there were six foundational pillars that we had to address. And we thought need to form the backbone of the collective roadmap for the future. computer capability collaboration connectivity compliance and context and I’ll just briefly speak on each one of these a bit compute no doubt is today’s defining barrier the access to GPUs accelerators high -performance clusters is a major issue for all AI ecosystems but the issue is how it can be made distributable affordable and reliable across and not concentrated in a few geographies and this would no doubt require us to look at whether compute can become a shared infrastructure in future or kind of a which supports public interest innovation and to the extent that we are focusing on innovation how that part can be a public interest infrastructure secondly infrastructure structure would not be sufficient there is a widening skills gap.
So how we can consider capability diffusion focusing on joint research, shared standards, open platforms and mutual learning. What needs to be done for this responsible deployment is so that we can link innovators to compute resources and citizens to trustworthy AI enabled services. Equally important would be governance. The governance framework needs to be robust enough to build trust, yet flexible enough to adapt to diverse social and cultural contexts. Open source and maybe modular AI stacks would help in enabling localization without creating dependency. So looking at some of these issues, on what mechanisms can be done to facilitate accessible and affordable computing resources by improving utilization rates and reducing transaction costs and also to lower barriers for access regardless of geography.
The working group looked at how this can be taken forward through a collaborative platform designed to expand shared access to compute data in partnerships. And the platform has been termed as Maitri, which is friendship in Hindi. Maitri, M -A -I -T -R -I, standing for Multi -Stakeholder AI for Trusted and Resilient Infrastructure, to be developed as a digital public good that countries can adopt, customize, and build upon. And obviously, it is a non -binding, voluntary, modular approach. depending on the context of each country, what kind of compute and what kind of methods can be used to have accessible, at least for innovators and researchers looking at data sets that can be put out, which are take care of the national laws and national protocols in place and look at models.
So what which are open source and which can be placed. So this this we envisage would help to at least ensure that portions of AI are a global public good, because we we we are focusing on innovation and research out here. And this would go beyond just a focus on hardware and platforms, but also in skills, institutions and governance capacity. I would just like to mention one area. The other is that the technology is a very important part of the development of the technology. but how perhaps it might proceed in future is also the fact that while infrastructure or compute seems to be the biggest constraint going forward as of now, that’s perhaps also based on the present models requiring large amounts of compute capacity and energy.
Going forward, would models retain this system of algorithms that they have, or would there be obviously small domain -specific niche models? I think yesterday there was a very nice remark made by Vishal Sikka, who mentioned that unlike when we talk of compute infrastructure, we are talking in terms of gigawatts, nothing less than that of whatever. But when you talk of a human being, you talk in terms of only 2 ,000 calories requiring a human being to sustain a computer. Which is not more than a 100 -watt bulb. for a day. So are we missing something out here and I think that’s a very important point that he made yesterday and that’s why the focus I think we need to have much more on the models and that itself might solve a lot of the areas that we are and when we’re talking of democratizing AI perhaps that’s the path forward.
So I’ll stop here and thank you all. Thank you for this opportunity.
We now transition to the panel discussion and may I request Andrew Sweet, VP at the Rockefeller Foundation who is the moderator for the panel. Please join us here on the stage. May I request other panelists, Dr. Shikogitao, Martin Martin Tisney Vilas to join us on stage. Yes and Sean sorry Sorry Andrew over to you
thank you Dr. Garg for those inspiring remarks and for the framing insight and perspective that you bring to this conversation all of the many conversations that you’ve had throughout the course of the week so we’re excited to continue and deepen the conversation today and very excited that we have five of the world’s brightest minds to discuss this topic these are all people that have been in the AI arena for decades, this is not new to them and all people that have deep regional expertise and global perspectives so very excited for this conversation today. We don’t have a lot of time we have about 25 or 30 minutes for the conversation so we’re going to dig in, we’re not going to have a number of speeches, Dr.
Garg’s speech will be the only speech that you’ve heard today but we’ll have a short series of provocations with actionable ideas for how we can move this agenda forward And so hopefully this conversation can be, you know, informal, back -and -forth banter. I think we’ll have one round of questions, but it would be great if we could kind of feed off of each other’s questions and energy because I know we all have a lot to say here on the panel and a lot of expertise to share. So I’ll briefly introduce the panelists, then we’ll dig in. You’ve already met Dr. Garg. He’s the Secretary of the Ministry of Statistics and Program Implementation for the Government of India.
He has been instrumental in shaping India’s AI governance and previously led the technology stack for the Transformative Adhar Initiative. We have Martin Tisné, founder of Current AI and public interest envoy for France’s AI Action Summit. Martin has spent 15 years building multi -stakeholder initiatives like the Open Government Partnership that we talked about earlier today to govern technology based on democratic values. We have Vilas Dhar , president of the Patrick J. McGovern Foundation. Vilas serves on the UN Secretary General’s High -Level Advisory Board on AI and leads one of the world’s largest philanthropic movements to AI for public purpose. my friend Dr. Shikoh Gitau founder and CEO of Kala AI a visionary from Kenya. She established Safaricom Alpha and has been a leading voice in ensuring that digital transformation in Africa solves real problems in education, healthcare and agriculture and finally, Shaun Seow CEO of Philanthropy Asia Alliance.
Sean is working to catalyze collaborative philanthropy across Asia, leveraging deep expertise from his time at Temasek and is CEO of Mediacorp so we’ll continue the conversation first question will go to Dr. Gerg India has launched the India AI mission with a target of 38 ,000 GPUs if we view compute as a public utility, much as we do with water and electricity what is the governance model that India is envisioning and should compute access be rationed or priced differently for public interest applications
so I would say that the focus is not on rationing but on intelligent prioritization I think that’s going to be the focus, that the impute capacity is an enabling platform, and as I mentioned, as a digital public good, at least that’s where innovation and research is going. So that we focus, and I think that’s where a lot of the philanthropic organizations would have a large role to play, given that their focus is also on ensuring that AI benefits all. So with that focus in view, how governments, philanthropic organizations, and the private sector can collaborate to ensure that affordable compute capacities are accessible to all. I think that’s the models that we are looking at, and that will ensure experimentation going forward.
Thanks, Dr. Garg. Martin, I’ll go over to you. Through current… AI and the Paris Charter, you’ve convened governments to discuss public interest AI. How do we move nations from being consumers to genuine co -creators? And quickly, you’ve also spoken about this looming data bottleneck. What do we do to unlock data sets for training without compromising privacy?
Okay, two big questions. Thank you. So, as you mentioned, we launched Current AI last year. We’ll be launching just this afternoon our first product, which is an open hardware product looking at linguistic diversity. I think I’ll be a little bit provocative to maybe start our session. I think compute is critical for obvious reasons. I think that from a financial, from an innovation, and from a sovereignty perspective, it is also possible to overplay it. I’ll tell you what my worry is, and I’d love to know what the panel thinks. I do have a worry that we could end up in a few years’ time in a world where we succeed in having compute capacity in inverted commas, in a number of countries, including in the global south, but where effectively the data centers are not used.
We’ve been talking to colleagues around the world. You do also have data centers that are effectively kind of white elephants and that are not used anywhere close to full capacity. And so I think for countries to be able to exercise sovereignty, they need to have contextual AI. They need to have contextual data in their languages with all of the diversity and the incredible richness that typifies their cultures available in order to create contextual localized AI that actually serves outcomes that people care about. And so while the compute piece is important, I think it’s one part of the issue. We need to talk about the data piece and we need to talk about the second part is the open source one.
So briefly, I think throughout the event, people talk about open source AI, that it’s a really good thing, that we’re all pro it. I think we also need to talk about how, from a philanthropic perspective, we resource the open source ecosystem. The reality of open source software is it’s mostly the top tier of open software is funded by large companies that are using it, right? Linux is partly funded effectively by volunteers working for SpaceX that are using it. There’s a bottom tier of dependencies in open source that are run on a shoestring, you know, by a few like critical, amazing people working overnight as volunteers. And there’s very few organizations, one of them, which is a part of the current AI roost, which looks at robust open source trust and safety, that are funding those critical dependencies.
So I think that for states across the world, in the global south and the north, to really be able to exercise sovereignty, and I’d love to talk about this a bit before, but I don’t want to hog the mic, we need to talk about compute, but also we need to be realistic about what the compute is going to be used for. So I think the data piece and the open source piece are really important. I think I’ve probably run out of time to talk about the data bottleneck.
Go for it.
Well, so the number… There are people in the room I’ve worked with for a long time on this issue. Velas, you’re one of them. Sushant, you’re another. I won’t name check everyone. I think it’s fantastic that there’s been so much innovation in compute and we’ve seen such change over the past 10 years. In contrast, I think it’s a complete tragedy that we haven’t seen as much, anywhere near as much innovation when it comes to data and specifically the ability for people to be able to share personal data in ways that both respect privacy and contribute to outcomes. And that’s effectively it. I think we need a huge amount more resources and thought, both when it comes to the technical side of the issue, and here, other than the side, I think that partly it’s solved, but enterprise users of AI have access to these kind of technical safeguards in a way that private users don’t.
And there’s a story that we can talk about if we have time. And then on the governance side, so for example, Velas, you and I have talked for a long time about different, and now there’s different forms of data stewardships, whether data trusts or others. To the day… I haven’t seen one that really scales to the level that we would want to see it scale. to and that I think we need a lot more resources, a lot more thinking there’s been work done but if we could harness even 20 % of the sort of like brain capacity of the world that’s going into compute right now I think we would be in a very different place. Thank you.
Excellent, thanks Martin. Actually Vilas I’ll go next to you because I think this reminds me a little bit about a recent article you wrote about the Indian Premier League as a model for how India builds world class institutions I re -read it this week in preparation for this conversation is there a similar IPL playbook for public interest compute or is the window for building these public institutions closing as commercial consolidation accelerates?
Well I can’t think of a more controversial topic to spend our time here in this conversation than cricket. It’s been a good week all around but I think many of the people in this room probably know. Before I start I just want to say Dr. Garg I want to acknowledge in particular your leadership on this work. I spend a lot of time with senior decision makers across governments and the conversations that we have had have really given me great hope for the combination of technological confidence but also an understanding of what this means across an ecosystem. And so I want to acknowledge your leadership in particular. Thank you. Look, this question around the IPL I think is great, right?
I mean, let’s not torture the analogy and take something really fun and then try to, like, tie it to AI. But here’s what I’ll say about it. I think in many ways what we need is a new institutional framework that goes from the elites who are participating in their own places to something that feels deeply participatory. And I think around compute infrastructure in particular, we are stuck in a model where we keep reengaging and renovating old concepts and try to describe a new world. I will tell you sovereignty has been the buzzy word of the moment, right? Everybody wants to talk about sovereignty and diffusion. Sovereignty as a Westphalian concept that goes back a few centuries tries to take the idea that ownership of pieces of silicon somehow magically results in outcomes and impact that transform lives.
Now, there are logical links. And, of course, there are codependents. competencies, but to simply say that we will site compute in a particular geography and so figure out a way to disconnect ourselves from the interdependence of the 21st century doesn’t really bring us to a good outcome. I’ll tell you the second part of this, AI diffusion. If you haven’t heard this already, every tech CEO here, this has been the buzzword of the moment. I spent some time yesterday with the prime minister and a number of tech CEOs who wanted to talk about their investments in India. Those investments in many ways followed the playbook of the PR press release. They were, we’re going to build a new data center, we’re going to invest in a new compute capacity.
But when you dig deep and you ask the next question, who will this really benefit? What value does this create for public impact and outcome? How does putting a large number of servers in a particular place result in that community finding an economic uplift, a benefit in economic opportunity, a sense of dignity? The conversation sometimes falls. flat. So AI diffusion to me in its core concept, the idea that you hyper concentrate technological capacity, compute data, and somehow the rest of the society benefits sounds a little too much like something that as an American I know too intimately as trickle -down economics. The idea that if we made the rich as rich as possible somehow the benefits would filter down to everybody else and it would work.
AI diffusion is a passive concept. It starts on the premise that we build technological capacity for a few and somehow it works out for everybody else. But there’s an alternate model and it ties directly to this report that’s been issued today and the work that we’ve been talking about. For AI to benefit everyone requires a direct and active impact. It requires us to step in and say what are the institutions we have to build that actually physically and metaphorically transform the idea of compute infrastructure to be something that everybody can use. It requires us to build the institutional layers and the capacity that lets a community that’s trying to solve a local problem know that compute isn’t the thing that holds them back.
rather the conceptualization of the problem the aggregation of the full stack of resource sets as Martin described that include compute that include data governance mechanisms that include the political agency of communities to participate and let us then turn that into that final app solution infrastructural development that actually leads to the outcome we’re solving for in many ways I think this is the great role of the institutions that are represented here on the stage and in this room for philanthropies to transform the capital landscape in a way that says great entrepreneurs and leaders like my dear friend Chico here and so many here in India that are building open source public access AI stacks don’t have to worry about the resource constraints of the private capital markets that they know that they can access governmental and substantive structural resources that let them build the tools that they want to and know that they have equitable access to the markets as well as a matter both of policy and as a product that they can go out and get to consumers and creators that they can provide a service that lets them people use it at scale.
And the last part of this, and I have to say this, is this doesn’t happen, as we’ve discussed in the private market, but it also doesn’t happen exclusively by going to frontline nonprofits and saying, now you’re supposed to be the builders. It requires us to innovate a new institutional set of intermediaries. I think of groups like Culpa Impact, which I think is an incredible example of a combination of technical sophistication, policy impact, support for government, that actually sits at the layer that connects these different elements and lets us build on top of it. I think this is the work that’s ahead. If we really think about pragmatic outcomes to this conversation, Andrew, I think one of the questions we might ask is, what are the institutions we need to build in the next 12 months that connect the dots around all of these different pieces and support this transformation at scale?
Dr. Shikha, I came across a recent article that you put out there saying that for the West, AI is a matter of efficiency, but for you, it’s a matter of life or death. You’ve been a champion for AI access. You were very active in this summit. You were very active in the Kigali Summit. We were together at the launch of the first ever AI factory for Africa together in April in Kigali. You’ve also talked about global tech companies. If they want African data, they should provide compute infrastructure in return. How do we formalize these reciprocal agreements, and what does a true India -Africa partnership look like that doesn’t just reciprocate global North -South models, similar to what Vilas was just talking about?
Thank you very much for having me. It’s always fun to listen to everyone here on this. I was hoping somebody was going to preempt some of the work that I was going to talk about, but lucky for me, I have some stuff to talk about. Thank you, Vilas. So when we talk about compute, it’s this amorphous thing. In fact, we launched an AI research lab in Nairobi, and we have some GPUs there. And one of the key things was like a demo showing up what a GPU is. And our PS was like, oh, my God, this is what a GPU is, because he’s never seen a GPU. And then I made sure, like, every time I’m speaking, I’m asking, how many of you actually have seen a GPU, not on the Internet, touched one?
Maybe five people. And this is everywhere. Every single room that we’re talking about compute, we ask the same question, have you ever seen a GPU? And so right now, five to ten people. So it’s this thing that people talk about. We need GPUs, we need compute, we need all of these things. And for us, it is very important, as an African continent, we had, like, our research. Colo came for the Global South a few days ago. Same question, how many of you have seen a GPU? about 10 people had never touched a GPU. How many of you need compute? Everybody raises their hand. But what does that actually mean? In fact, one of the panels, the starting point was like, when it comes to compute, we all need Jesus.
And I thought, how do we quantify this? So we, and I think we have already spoken to Calpa about this. We’re working, I think, on the same time about a framework. So we just released a compute demand index. Because we realized every time we speak about compute, people have ideas, they have thoughts, they have proposals. They don’t have the numbers to say that. We need GPUs. How many? We need megawatts. And the gigawatt, megawatt conversation, what does a gigawatt of compute actually mean? So we went ahead and said, for Africa, we need to, every time we’re having conversations with these governments, this is actually what you need. But you actually need to put money into it.
So we, our first index was, did it demand? And the second one is, is your country ready for this? which we are calling AI Investment Readiness Index. So I’ll give you some numbers. Africa needs 2 .5 million hours of GPU hours a year, 7 .5 million for the next three years to be able to start computing well. This is for training as well as research. That is something that I can work with. So when I come to India and say I need 2 .5 million GPU hours a year, how many of them can you give me? And we have this conversation with the UNDP in Italy, and they said, oh, we have 1 .5 million GPU hours that we can donate. We have 1 .1 million more to go.
Cassava is saying we are putting 2 ,000 GPUs. How many hours of GPUs with those GPUs, hours, not actual physical, how many hours of GPUs without those 2 ,000 GPUs actually provide for the continent? So we need to be. We have to start being very practical rather than being arbitrary on what we. want. Of these 7 million GPU hours we need in the next three years, Africa only has 5 % of that. So we are doing the math. We only have 125 ,000 of these GPU hours a year, which is like times three of that for the next three years. So you’re solving, when I go to villas, I’m saying I need these GPU hours. It’s very practical as I’ll say I can be able to do half a million GPU hours.
So it’s not just going with an arbitrary number. I need GPU hours to be able to put this. And for us, that is important. But for me, it is the conversation about investment, and that’s the conversation that we asked. How do we have this South -South collaboration? How does we have this India collaboration? How does it actually look like? There’s the paradox. Everybody, as he said, as Martin said, everybody wants sovereignty. Everyone wants to talk about diffusion of AI. But what does that actually mean? Do we actually need it? So I’ll give you two examples of my two favorite countries. Hopefully none of them is here, actually in Nigeria. So there’s something you’re calling the Nigerian paradox.
Nigeria is the number one country in the computer demand index. Why? Nigeria is doing very well. It’s 110 million Internet users, a huge population. They’re doing very well in terms of, like, e -commerce, financial services. So they’re up there when it comes to why they need compute. And we’ve seen this in India. India is very high there as well when you’re doing the same exact thing. But what about investment readiness? And investment readiness is are they able or capable of running a compute facility? Do they have power? Do they have the talent? And I love, I think, what Mateen and the minister spoke about, what we think about. When you think about compute, you don’t think about just GPUs.
It’s a whole stack of things. It’s talent. It’s governance. It’s all these things. And you’re thinking about. When you think about investment readiness when it comes to compute, you have to look about all these things. Because I can give you GPUs, as he said, and I’ve worked in digital transformation for the last 20 years, and I work for the AFDB as a digital transformation lead, and we’ll buy computers and go three years later, and they have never been powered at all. And that’s the case that is going to be with GPUs, because you’re going to give countries these GPUs. If they don’t have talent, they don’t have the power to run it, they don’t have the data sets, they don’t have models, they don’t have use cases to build on top of it, you’re wasting that money.
And that’s where the investment readiness comes in. So we’re talking to countries, and we’ve had this conversation with African countries, is there’s no point of investing all your dollars in putting a compute facility. Get your talent ready. Get your data sets ready. Have strong use cases that people can back. Then, with all of that, can we then define what are the demands? Can we make money? you need. You don’t need a gigawatt Kenya, you do not need a gigawatt of compute to be able to run. Maybe you need a 200 megawatt facility and that’s where we want. So coming back to the question, how do we interact with India? This is our demand. Burundi might need 50 megawatts of GPU.
Can India be able to facilitate that? But it’s not just about facilitating the GPU, it’s what is the GPU in service of solving for health, education, agriculture. And when you have clear use cases, then the GPU demand becomes an obvious ask. And I think bridging that and convincing governments especially of bridging that gap is what we need to be able to do. And then the governance framework actually comes to play. Thank you. I know that’s a lot.
That’s great. Thank you. Thank you, Dr. Shikha. We’ll go through Sean, and then I want to keep it kind of informal for the remaining ten minutes after Sean speaks, so any reactions to any of the comments, and then we can do a lightning round if we have time, but if we don’t have time, that’s fine as well. Sean, over to you. The Philanthropy Asia Alliance brings together 80 members and partners to address Asia’s interconnected challenges through collaborative philanthropy. Is there an opportunity for Asia’s philanthropic networks to coordinate shared compute and infrastructure, pulling resources from places like India, Indonesia, and other nations, rather than competing, and what would unlock that collaboration?
Thanks, Andrew. The advantage of coming last is that I could say I agree with all of them. Actually, I’m going to add to the much maligned word called compute. Maybe we could end the panel right away. I’m going to join Martin in actually agreeing that compute is actually a bit overrated. The ownership… of compute… So when you think about the stack, I’m going to add another way to frame the conversation, Jensen Huang’s AI stack. When you think about energy, hardware, compute models and applications and the top layer applications is really what will drive and value capture for the economics as well as the impact, social impact. Really the stumbling block is probably energy at a bottom level.
And thankfully for many countries in Asia, the costs have been driven down because of the abundance of hydro, solar and wind. Then when you think about the next layer of hardware, I mean that’s obviously dominated by China, Chinese and American players. And when you think about the compute level, I understand why we fuss over compute because the Americans own 75 % of the GPU cluster performance. The Chinese 15%, Europeans maybe about 4%. and the rest of us are only like 0 .1%. I think even India is just 1 % of that. But I think the issue is actually deeper than just the ownership. I mean, if you think about what it needs to get the work done, it’s more access. So the question you’ve posed me about sharing of compute, for example, between Indonesia and India, I live in Southeast Asia, and that’s why Indonesia is like a couple of hours away from where I live.
And we know the situation in Indonesia quite intimately. There’s data residency requirements, and that’s why there’s a build -out of data centers. Think also of the physical limitations of actually the latency of sharing compute between India and Indonesia. For example, 10 ,000 kilometers apart, when you think about the latency of what, 50 to 100 milliseconds, it’s just not going to work for the sharing of compute between Indonesia. and India. Attractive as the idea is, it doesn’t work. I think they’re just physical limitations, data sovereignty, privacy issues that prevent that from happening. So I just want to look at the positive side of what’s happening. When you think about the cost of compute is coming down, when you think about the emergence of new clouds, GPU for a service, I think these developments are actually going to be good for the unleashing of AI, for social impact, for economic capture.
So the way you can think about it is, how do we then make it a little bit more accessible for startups, for impact organizations? Maybe the way to think about it is really, how do you think about aggregating demand so that you can actually negotiate with the new cloud providers and get a cheaper pricing? How do you then think about philanthropy coming in? And to subsidize some of the compute costs. and I think I kind of agree more with the observation that you really need to go beyond just infrastructure you need to think about the ecosystem you’re building I think the skills gap in Asia is actually huge and that could be really what’s stopping us from really optimizing maximizing the power of AI in what we want to do Is that too long?
No, that’s perfect I’m not sure if anybody wants to react to any of that Martin, I see you scribbling furiously maybe first reaction to you
No, I am scribbling. I’m scribbling because I’m thinking about your points I’m thinking about the points of the panel and I’m thinking about the term sovereignty So my scribbles are to your point about the Westphalian concept of sovereignty that’s about the ability to make law within your territory and it’s a very global north concept and it’s a notion of territory which has physicality and I’m just what I was scribbling was the physicality of the territory, it’s like we’re very focused on the physicality as you were saying on the GPUs of the bricks and mortar, so we’re going to be okay because we’re going to be sovereign on this data centre, the data centre it’s on my territory and what got me thinking is other concepts of sovereignty such as when I was spending a lot of work working on data calamities and data stewardship, thinking about indigenous data sovereignty which is a different type of concept it’s a more relational concept than a territorial concept, right?
It’s about a pre -existing an inherent authority a relational authority over that which makes up a people, and so when we were studying for example indigenous data sovereignty in the Maori context in New Zealand the Maori community any data that in any way involves Maori, the Maori community … legacy is part of the Maori community so I think that there’s something here in thinking about a very in some ways a quite rigid approach to sovereignty which is about control as mentioned and one which is more about agency and which is more relational so that’s what the panel has got me thinking and I’ve been doing some writing and thinking with colleagues and friends around the notion not of a sort of like a controlled national stack but a global open resilient collaborative stack and that’s not one at all and just I’ll finish with that, that doesn’t mean that like all the data is open and anything goes and anyone can extract your personal data and you’re back in a sort of Zuboff you know surveillance capitalism world it’s one where it’s a question of choice and agency and the what you wish to exercise authority over and how.
That’s my scribbles. Thank you.
As you can tell, when we get on a panel with people you love and respect, the conversation just flows. So I want to build off this point and a little bit of what you said, Chico. I want to take a different tack to this question of agency, which is if I had asked any development leader in the world 10 years ago, if you could have your dream of an extra gigawatt of energy capacity in your country, what would you do with it? I can’t imagine that any of them would have said, well, I want to use it to run a bunch of computation on things that may or may not have short -term economic value for my country.
Andrew, your organization has been incredible around the world at building capacity and grids in power production, in ensuring that people can use power for development. And yet somehow, I think for many of us, we are surrounded by conversations where now the question has become, how many megawatts and gigawatts can you put into compute for AI? It is a fundamental challenge when you think about what are our priorities in development. Again, going back to core principles of human rights and dignity and participation in the world, to say governments who have limited capacity should now all of a sudden be focused on this topic. It brings us again from this question and this shift. In many ways, I acknowledge that the traditional conversation around compute is one of breaking over -dependence on the American AI stack, on other international players that are coming in.
But the response to over -dependence isn’t internal dependence, it’s interdependence. It’s saying if there are places that have incredible capacity and even potential to drive, as Shiko said, the availability of compute hours, how do we build interconnectedness that lets that be a mutual value exchange? Not merely, again, clients who have to go to another country and say, please give or let us buy compute, but rather the products of that compute are going to be to build the infrastructure that you can then use in your own country. To allow for centers of excellence that allow for local capacity and local competence to drive what gets built and to allow that to be the new tokens of international trade in a way that leads to a much more connected and shared prosperity rather than descending back to that 200 -year -old concept of how do we make sure that we’re competitive in an adversarial frame.
I recognize that what I’ve just shared with you is maybe not where the dominant private sector conversation is. And to those who would oppose it, the primary critique is, well, that sounds quite naive. And yet we’ve seen it happen. We’re seeing it in the few areas of hope in the multilateral system where we’re actually finding that technology governance is something that brings everybody to the table, that lets people engage in meaningful shared outcomes. We’re seeing the seeds of it. The question is whether we’re going to let them sort of die out in the sun or if we’re actually going to water them, invest in them, in order to grow.
Great. Dr. Garg, any final insights?
I know there’s a little time, but just one thing I would say that perhaps we need to spend a bit more time going forward on the frameworks that will help ensure public interest frameworks looking at things beyond compute, looking at models, looking at talent, looking at data, how that can be shared and interoperable and in a manner which takes care of public interest. So I’ll just stop it out there.
Well, thank you. Thank you to the Indian government. thanks to our partners at CalPA for putting this together especially for the authors this is now officially out there I think you have until March 31st to read the copies are available out there you have until March 31st to review the document and submit your reactions thank you to the panelists, really appreciate it enjoy the rest of the summit, thank you I think the NDIA team wants to hand over some souvenirs from the panel Thank you Thank you. Thank you. you you Thank you. Thank you.
“India’s AI mission is mobilising more than 38,000 public‑sector GPUs to create one of the world’s most ambitious sovereign‑yet‑open compute ecosystems for the Global South.”
The knowledge base states that India is building one of the world’s most ambitious public-interest compute ecosystems with 38,000 GPUs as public infrastructure, confirming the reported figure [S1] and the similar description in [S14].
“The AI Summit’s three guiding “sutras” – people, planet and progress – and the mandate that “AI must serve human welfare, advance sustainable development and enable shared prosperity”.”
Multiple sources record the summit’s three sutras (people, planet, progress) and the associated mandate, matching the report’s wording [S109] and further echoed in [S110] and [S111].
“India’s AI mission is mobilising more than 38,000 public‑sector GPUs to create a sovereign‑yet‑open compute ecosystem.”
While the current deployment is around 38,000 GPUs, the knowledge base notes that India plans to expand its public-infrastructure to 50-60,000 GPUs, providing additional context on the programme’s scaling trajectory [S48].
There is strong consensus that compute access, governance, data sharing, philanthropy and South‑South collaboration are pivotal for democratizing AI. Speakers align on the need for intelligent prioritisation, robust multi‑stakeholder institutions and holistic readiness beyond hardware.
High consensus across most themes, indicating a solid foundation for coordinated action on public‑interest AI; the main divergences relate to technical feasibility of cross‑border compute sharing and the relative emphasis on hardware versus systemic ecosystem factors.
The panel broadly agrees on the need to democratize AI resources and to involve philanthropy and new institutions, but diverges on where the primary bottleneck lies (compute vs data vs broader readiness), on the feasibility of cross‑border compute sharing, and on the conceptualisation of sovereignty and governance. These disagreements reflect differing priorities—technical feasibility, policy design, and institutional architecture—rather than outright conflict.
Moderate disagreement: while participants share common goals, they propose contrasting pathways (e.g., compute‑centric infrastructure versus data‑centric governance, technical infeasibility versus South‑South collaboration). The implications are that any collective action will need to reconcile these perspectives, likely through hybrid approaches that address compute, data, talent, and governance together.
The discussion was driven forward by a series of pivot points that moved the conversation from a high‑level narrative about compute scarcity to concrete, multidimensional solutions. Deepali’s framing of a ‘compute divide’ and India’s GPU programme set the stage, but it was Dr. Garg’s MAITRI platform and the six‑pillar framework that gave the panel a tangible reference. Martin’s warning about ‘white‑elephant’ data centres and Vilas’s critique of simplistic sovereignty reframed the problem as one of data, open‑source ecosystems, and institutional design. Dr. Gitao’s demand and readiness indices grounded the debate in measurable gaps, while Shaun’s practical take on energy, latency and demand aggregation reminded the group of operational limits. Together, these comments redirected the dialogue from hardware‑only solutions to a holistic view that includes governance, talent, data stewardship, and new intermediary institutions, shaping a richer, action‑oriented conversation.
Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.
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